更改R中数据框列中的所有值

Question

I have a large dataset of GCM data from the USGS GeoData portal. 我有一个来自USGS GeoData门户的GCM数据的大型数据集。 I need to convert the dataset to a form for VIC 4.2 forcing files. 我需要将数据集转换为VIC 4.2强制文件的形式。

Below is an image of the top of the GCM data for illustrative purposes: 以下是GCM数据顶部的图像，用于说明目的：

GCM dataset GCM数据集

The temperature data is in Kelvin and I need it in Celsius for VIC 4.2. 温度数据以开尔文（Kelvin）为单位，而对于VIC 4.2，我需要以摄氏度为单位。

Here is my code: 这是我的代码：

# Script for createing a VIC 4.2 forcing file fromd the GCM data from the USGS:GeoData Portal
#
# Read the file. File is date, precip, MaxT, MinT, wind speed for entire year 2099
df <- read.csv("/Users/CoyoteGulch/Documents/ClearCreek/data-2.csv", header = TRUE, sep = ",")
#
# Subset the data frame and convert K to C for the VIC 4.2 forcing file
#
df.forcing <- df.forcing <- df[15:379, 2:5]
#
# Convert the temperature columns from Kelvin to Celsius
df.forcing$X.1 <- df.forcing$X.1 - 273.15
df.forcing$X.2 <- df.forcing$X.2 - 273.15

When I run the script I get two warnings related to the last two lines: 运行脚本时，我收到与最后两行有关的两个警告：

Warning messages: 1: In Ops.factor(df.forcing$X.1, 273.15) : '-' not meaningful for factors 2: In Ops.factor(df.forcing$X.2, 273.15) : '-' not meaningful for factors 警告消息：1：在Ops.factor（df.forcing $ X.1，273.15）中：“-”对因素无意义2：在Ops.factor（df.forcing $ X.2，273.15）：“-”中无对因素有意义

In addition all of the values in columns X.1 and X.2 are changed to "NA". 此外，X.1和X.2列中的所有值都更改为“ NA”。

Results from dput(df.forcing) dput（df.forcing）的结果

dput(df.forcing) structure(list(X = structure(c(2L, 211L, 116L, 50L, 2L, 88L, 2L, 2L, 19L, 22L, 62L, 2L, 2L, 2L, 2L, 215L, 101L, 148L, 191L, 155L, 104L, 210L, 111L, 2L, 66L, 55L, 2L, 2L, 2L, 18L, 214L, 92L, 169L, 185L, 53L, 221L, 73L, 186L, 130L, 106L, 212L, 132L, 89L, 2L, 216L, 140L, 167L, 135L, 125L, 223L, 188L, 225L, 2L, 128L, 70L, 35L, 51L, 2L, 133L, 192L, 159L, 75L, 85L, 64L, 160L, 2L, 2L, 91L, 105L, 2L, 38L, 113L, 40L, 195L, 213L, 157L, 96L, 220L, 176L, 141L, 134L, 2L, 2L, 158L, 179L, 180L, 182L, 48L, 2L, 2L, 147L, 24L, 227L, 39L, 2L, 2L, 2L, 36L, 230L, 139L, 172L, 224L, 120L, 196L, 184L, 232L, 126L, 202L, 122L, 152L, 204L, 205L, 78L, 171L, 219L, 201L, 2L, 2L, 2L, 2L, 112L, 127L, 124L, 37L, 28L, 2L, 2L, 2L, 43L, 228L, 107L, 231L, 2L, 2L, 181L, 206L, 86L, 136L, 129L, 208L, 15L, 25L, 143L, 2L, 93L, 199L, 49L, 2L, 2L, 2L, 2L, 2L, 8L, 41L, 2L, 2L, 103L, 10L, 2L, 2L, 2L, 173L, 115L, 27L, 163L, 69L, 2L, 2L, 117L, 229L, 197L, 30L, 31L, 72L, 174L, 94L, 2L, 198L, 67L, 95L, 119L, 154L, 63L dput（df.forcing）结构（list（X = structure（c（2L，211L，116L，50L，2L，88L，2L，2L，19L，22L，62L，2L，2L，2L，2L，215L，101L， 148L，191L，155L，104L，210L，111L，2L，66L，55L，2L，2L，2L，18L，214L，92L，169L，185L，53L，221L，73L，186L，130L，106L，212L，132L， 89L，2L，216L，140L，167L，135L，125L，223L，188L，225L，2L，128L，70L，35L，51L，2L，133L，192L，159L，75L，85L，64L，160L，2L，2L， 91L，105L，2L，38L，113L，40L，195L，213L，157L，96L，220L，176L，141L，134L，2L，2L，158L，179L，180L，182L，48L，2L，2L，147L，24L， 227L，39L，2L，2L，2L，36L，230L，139L，172L，224L，120L，196L，184L，232L，126L，202L，122L，152L，204L，205L，78L，171L，219L，201L，2L， 2L，2L，2L，112L，127L，124L，37L，28L，2L，2L，2L，43L，228L，107L，231L，2L，2L，181L，206L，86L，136L，129L，208L，15L，25L， 143L，2L，93L，199L，49L，2L，2L，2L，2L，2L，8L，41L，2L，2L，103L，10L，2L，2L，2L，173L，115L，27L，163L，69L，2L， 2L，117L，229L，197L，30L，31L，72L，174L，94L，2L，198L，67L，95L，119L，154L，63L , 42L, 33L, 59L, 20L, 16L, 6L, 4L, 45L, 13L, 187L, 149L, 9L, 11L, 207L, 217L, 2L, 3L, 74L, 54L, 32L, 2L, 123L, 137L, 164L, 58L, 190L, 161L, 178L, 203L, 97L, 2L, 222L, 12L, 2L, 2L, 65L, 168L, 109L, 71L, 99L, 183L, 2L, 52L, 2L, 2L, 153L, 175L, 2L, 2L, 2L, 2L, 5L, 44L, 81L, 87L, 2L, 2L, 2L, 2L, 2L, 34L, 80L, 2L, 14L, 114L, 2L, 68L, 150L, 83L, 2L, 2L, 2L, 2L, 76L, 56L, 2L, 2L, 2L, 2L, 2L, 2L, 21L, 110L, 2L, 2L, 2L, 2L, 2L, 100L, 2L, 2L, 131L, 156L, 2L, 2L, 2L, 2L, 90L, 7L, 2L, 2L, 29L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 189L, 200L, 60L, 2L, 2L, 47L, 46L, 2L, 2L, 2L, 98L, 177L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 17L, 2L, 2L, 2L, 226L, 166L, 218L, 209L, 121L, 79L, 84L, 23L, 2L, 2L, 2L, 2L, 2L, 138L, 233L, 77L, 146L, 165L, 61L, 57L, 142L, 162L, 170L, 118L, 82L, 2L, 194L, 2L, 2L, 2L, 26L, 2L, 145L, 108L, 234L, 151L, 193L, 102L, 2L, 2L, 2L, 2L, 144L, 2L, 2L, 2L), .Label = c("", "0", "0.051353", "0.053209", "0.053888", "0.054528", "0.055005", "0.055404", "0.055677", "0.056041", "0.064086", "0. ，42L，33L，59L，20L，16L，6L，4L，45L，13L，187L，149L，9L，11L，207L，217L，2L，3L，74L，54L，32L，2L，123L，137L，164L，58L 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"1.937468", "1.98256", "10.06452", "10.158648", "10.239129", "10.346543", "10.387987", "10.3915", "10.512586", "10.560392", "10.675141", "11.08884", "11.37613", "11.972711", "13.477461", "13.50885", "13.797744", "13.891509", "15.740662", "15.806064", "16.130033", "17.531771", "17.64015", "18.093937", "2.037883", "2.100556", "2.111043", "2.242924", "2.286656", "2.28742", "2.334287", "2.336922", "2.370191", "2.410635", "2.416346", "2.438619", "2.537189", "2.597875", "2.644497", "2.67492", "2.716421", "2.782552", "2.78437", "2.797231", "2.801501", "2.831995", "2.972122", "2.980867", "21.684855", "23.33168", "3.078672", "3.096159", "3.160731", "3.268467", "3.281176", "3.283479", "3.425798", "3.548023", "3.577743", "3.621364", "3.631743", 36”，“ 1.520051”，“ 1.549106”，“ 1.567124”，“ 1.568529”，“ 1.591836”，“ 1.607509”，“ 1.616776”，“ 1.618228”，“ 1.658783”，“ 1.668356”，“ 1.701482”，“ 1.724571” ，“ 1.733679”，“ 1.761405”，“ 1.770481”，“ 1.77509”，“ 1.810844”，“ 1.849057”，“ 1.863717”，“ 1.877862”，“ 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5.160832“，” 5.192636“，” 5.221751“，” 5.241645“，” 5.270457“，” 5.346045“， “ 5.414251”，“ 5.801967”，“ 5.834321”，“ 6.018086”，“ 6.034756”，“ 6.060279”，“ 6.343235”，“ 6.375944”，“ 6.417311”，“ 6.447439”，“ 6.569529”，“ 6.662778”，“ 6.672312” “，” 7.124838“，” 7.171772“，” 7.422026“，” 7.448046“，” 7.538418“，” 7.547452“，” 7.628211“，” 7.936975“，” 8.028339“，” 8.037“，” 8.197443“，” 8.217916“， “ 8.289074”，“ 9.113519”，“ 9.260917”，“ 9.393965”，“ 9.397649”，“ 9.887436”，“ pr_GFDL-ESM2M_rcp45（mm）”），类=“因数”），X.1 =结构（c（21L ，18L，5L，15L，7L，23L，90L，93L，61L，73L，96L，109L，122L，92L，100L，30L，67L，79L，94L，88L，10L，8L，39L，33L，27L，31L ，83L，68L，52L，77L，47L，64L，89L，55L，62L，70L，99L，170L，147L，152L，121L，125L，103L，146L，113L，81L，40L，9L，26L，24L，20L ，12公升1 7L, 4L, 11L, 37L, 59L, 105L, 116L, 53L, 51L, 75L, 54L, 56L, 58L, 65L, 135L, 156L, 144L, 153L, 148L, 149L, 184L, 163L, 115L, 95L, 66L, 43L, 48L, 46L, 35L, 42L, 107L, 91L, 72L, 44L, 29L, 50L, 28L, 137L, 175L, 167L, 140L, 108L, 166L, 183L, 206L, 253L, 229L, 179L, 150L, 84L, 102L, 78L, 120L, 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2.939846”，“ 2.940798” “，” 2.946666“，” 2.9572“，” 2.98643“，” 3.002297“，” 3.00517“，” 3.056561“，” 3.058981“，” 3.060029“，” 3.077511“，” 3.082538“，” 3.095381“，” 3.110173“， “ 3.127519”，“ 3.156171”，“ 3.166575”，“ 3.194167”，“ 3.198972”，“ 3.230315”，“ 3.233357”，“ 3.234677”，“ 3.279899”，“ 3.292216”，“ 3.29559”，“ 3.322748”，“ 3.325321” “，” 3.365275“，” 3.370972“，” 3.402015“，” 3.402179“，” 3.404078“，” 3.406688“，” 3.431145“，” 3.434803“，” 3.530465“，” 3.546225“，” 3.568231“，” 3.595263“， “ 3.596859”，“ 3.621116”，“ 3.662884”，“ 3.68073”，“ 3.686013”，“ 3.686079”，“ 3.700039”，“ 3.709341”，“ 3.71924”，“ 3.731145”，“ 3.780532”，“ 3.832445”，“ 3.849474” “，” 3.854805“，” 3.862742“，” 3.897382“，” 3.921543“，” 3.921577“，” 3.938214“，” 3.942264“，” 3.942419“，” 3.945301“，” 3.973213“，” 3.974383“，” 4.001139“， “ 4.016488”，“ 4.030596”，“ 4.031099”，“ 4.031496”，“ 4.044111”，“ 4.057593”，“ 4.074”，“ 4.099645”，“ 4.100109”，“ 4 .107349", "4.110504", "4.173357", "4.180363", "4.189736", "4.203447", "4.251505", "4.262193", "4.274528", "4.299968", "4.313471", "4.333906", "4.362791", "4.365851", "4.376681", "4.378855", "4.399515", "4.408618", "4.447083", "4.478985", "4.540248", "4.547806", "4.589968", "4.596299", "4.616023", "4.659328", "4.678977", "4.768427", "4.776873", "4.787859", "4.816487", "4.890985", "4.902739", "5.013013", "5.027411", "5.034097", "5.037682", "5.068965", "5.088162", "5.096173", "5.147941", "5.168827", "5.171349", "5.328141", "5.365369", "5.38986", "5.399864", "5.428233", "5.514956", "5.62412", "5.74389", "6.545115", "6.747962", "7.099587", "was_GFDL-ESM2M_rcp45(ms-1)"), class = "factor")), row.names = 15:379, class = "data.frame") .107349”，“ 4.110504”，“ 4.173357”，“ 4.180363”，“ 4.189736”，“ 4.203447”，“ 4.251505”，“ 4.262193”，“ 4.274528”，“ 4.299968”，“ 4.313471”，“ 4.333906”，“ 4.362791” “，” 4.365851“，” 4.376681“，” 4.378855“，” 4.399515“，” 4.408618“，” 4.447083“，” 4.478985“，” 4.540248“，” 4.547806“，” 4.589968“，” 4.596299“，” 4.616023“， “ 4.659328”，“ 4.678977”，“ 4.768427”，“ 4.776873”，“ 4.787859”，“ 4.816487”，“ 4.890985”，“ 4.902739”，“ 5.013013”，“ 5.027411”，“ 5.034097”，“ 5.037682”，“ 5.068965” “，” 5.088162“，” 5.096173“，” 5.147941“，” 5.168827“，” 5.171349“，” 5.328141“，” 5.365369“，” 5.38986“，” 5.399864“，” 5.428233“，” 5.514956“，” 5.62412“， “ 5.74389”，“ 6.545115”，“ 6.747962”，“ 7.099587”，“ was_GFDL-ESM2M_rcp45（ms-1）”），class =“ factor”）），row.names = 15：379，class =“ data.frame “）

Answer 1

Your problem is that you have read from your csv file with strings as factors. 您的问题是您已经从csv文件中读取了字符串作为因素。

Try using this to read your data, so the values stay numeric 尝试使用它来读取您的数据，以便值保持数字

df <- read.csv("/Users/CoyoteGulch/Documents/ClearCreek/data-2.csv", 
               header = TRUE, sep = ",", stringsAsFactors = FALSE)

Answer 2

All columns of df.forcing are factor s. df.forcing所有列都是factor s。

str(df.forcing)
#'data.frame':  365 obs. of  4 variables:
# $ X  : Factor w/ 235 levels "","0","0.051353",..: 2 211 116 50 2 88 2 2 19 22 ...
# $ X.1: Factor w/ 366 levels "","259.428589",..: 21 18 5 15 7 23 90 93 61 73 ...
# $ X.2: Factor w/ 367 levels "","242.450241",..: 20 29 5 8 14 16 26 81 72 50 ...
# $ X.3: Factor w/ 367 levels "","0.308711",..: 321 358 322 134 35 36 356 351 345 350 ...

Convert to numeric with 转换为numeric

library(dplyr)
df.forcing <- mutate_all(df.forcing, function(x) as.numeric(as.character(x)))
str(df.forcing)
#'data.frame':  365 obs. of  4 variables:
# $ X  : num  0 6.376 1.878 0.464 0 ...
# $ X.1: num  265 264 261 263 262 ...
# $ X.2: num  251 253 246 247 249 ...
# $ X.3: num  4.275 5.39 4.3 1.92 0.868 ...

Then convert from Kelvin to Celsius as before. 然后像以前一样从开尔文转换为摄氏。

更改R中数据框列中的所有值

问题描述

Results from dput(df.forcing) dput（df.forcing）的结果

2 个解决方案

解决方案1
1 2018-07-12 22:08:04

解决方案2
0 已采纳 2018-07-12 22:06:43

更改R中数据框列中的所有值

问题描述

Results from dput(df.forcing) dput（df.forcing）的结果

2 个解决方案

解决方案1 1 2018-07-12 22:08:04

解决方案2 0 已采纳 2018-07-12 22:06:43

解决方案1
1 2018-07-12 22:08:04

解决方案2
0 已采纳 2018-07-12 22:06:43